Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f1270119358>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f127003ef28>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
    
    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    """
    Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
    Paper: https://arxiv.org/abs/1511.06434
    Example: https://github.com/carpedm20/DCGAN-tensorflow
    """
    
    with tf.variable_scope('discriminator', reuse=reuse):
        # Hidden layer
        h1 = tf.layers.conv2d(images, 64, 5, 2, 'same')
        # Leaky ReLU
        h1 = tf.maximum(alpha * h1, h1)
        
        h2 = tf.layers.conv2d(h1, 128, 5, 2, 'same')
        h2 = tf.layers.batch_normalization(h2, training=True)
        h2 = tf.maximum(alpha * h2, h2)
        
        h3 = tf.layers.conv2d(h2, 256, 5, 1, 'same')
        h3 = tf.layers.batch_normalization(h3, training=True)
        h3 = tf.maximum(alpha * h3, h3)
        
        h4 = tf.layers.conv2d(h3, 512, 5, 1, 'same')
        h4 = tf.layers.batch_normalization(h4, training=True)
        h4 = tf.maximum(alpha * h4, h4)
        
        flat = tf.reshape(h4, (-1, 7*7*512))
        
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse=not is_train):
        
        h1 = tf.layers.dense(z, 7*7*512)
        h1 = tf.reshape(h1, (-1, 7, 7, 512))
        h1 = tf.maximum(alpha * h1, h1)
    
        h2 = tf.layers.conv2d_transpose(h1, 256, 3, 1, 'same')
        h2 = tf.layers.batch_normalization(h2, training=is_train)
        h2 = tf.maximum(alpha * h2, h2)
    
        h3 = tf.layers.conv2d_transpose(h2, 128, 3, 1, 'same')
        h3 = tf.layers.batch_normalization(h3, training=is_train)
        h3 = tf.maximum(alpha * h3, h3)
        
        h4 = tf.layers.conv2d_transpose(h3, 64, 3, 2, 'same')
        h4 = tf.layers.batch_normalization(h4, training=is_train)
        h4 = tf.maximum(alpha * h4, h4)
    
        # Logits and tanh output
        logits = tf.layers.conv2d_transpose(h4, out_channel_dim, 3, 2, 'same')
        out = tf.tanh(logits)
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    smooth = 0.1
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits
                                 (logits=d_logits_real, 
                                  labels=tf.ones_like(d_model_real) * (1 - smooth)))

    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits
                                 (logits=d_logits_fake, 
                                  labels=tf.zeros_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits
                            (logits=d_logits_fake, 
                             labels=tf.ones_like(d_model_fake)))
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    # Get the trainable_variables, split into G and D parts
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]

    d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    
    assign_op  = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    concat  = [assign_op for assign_op in assign_op if assign_op.name.startswith('generator')]
    
    with tf.control_dependencies(concat ):
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    input_real, input_z, _ = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    step_count = 0
    step_print = 10
    step_example = 100
    losses = []
    show_n_images = 25
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                
                # Get images
                step_count += 1
                batch_images = batch_images * 2
                
                 # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z})
                
                if step_count % step_print == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                        "Discriminator Loss: {:.4f}...".format(train_loss_d),
                        "Generator Loss: {:.4f}".format(train_loss_g))    
                    
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))
                    
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 3.6770... Generator Loss: 0.1870
Epoch 1/2... Discriminator Loss: 1.1693... Generator Loss: 10.7039
Epoch 1/2... Discriminator Loss: 0.4893... Generator Loss: 7.1367
Epoch 1/2... Discriminator Loss: 0.4338... Generator Loss: 3.2043
Epoch 1/2... Discriminator Loss: 1.0865... Generator Loss: 4.3418
Epoch 1/2... Discriminator Loss: 1.9370... Generator Loss: 1.6446
Epoch 1/2... Discriminator Loss: 2.1195... Generator Loss: 0.9848
Epoch 1/2... Discriminator Loss: 4.4968... Generator Loss: 12.6594
Epoch 1/2... Discriminator Loss: 4.5547... Generator Loss: 0.0332
Epoch 1/2... Discriminator Loss: 2.1300... Generator Loss: 0.4174
Epoch 1/2... Discriminator Loss: 1.1210... Generator Loss: 5.3926
Epoch 1/2... Discriminator Loss: 0.8232... Generator Loss: 3.6381
Epoch 1/2... Discriminator Loss: 0.5788... Generator Loss: 2.5897
Epoch 1/2... Discriminator Loss: 1.2973... Generator Loss: 0.6602
Epoch 1/2... Discriminator Loss: 1.0294... Generator Loss: 0.9483
Epoch 1/2... Discriminator Loss: 0.6329... Generator Loss: 1.8985
Epoch 1/2... Discriminator Loss: 0.7241... Generator Loss: 1.5383
Epoch 1/2... Discriminator Loss: 0.5163... Generator Loss: 2.3923
Epoch 1/2... Discriminator Loss: 0.5661... Generator Loss: 3.6936
Epoch 1/2... Discriminator Loss: 2.5246... Generator Loss: 0.2683
Epoch 1/2... Discriminator Loss: 0.7496... Generator Loss: 4.6925
Epoch 1/2... Discriminator Loss: 0.5098... Generator Loss: 2.1865
Epoch 1/2... Discriminator Loss: 0.4918... Generator Loss: 2.2671
Epoch 1/2... Discriminator Loss: 0.5006... Generator Loss: 3.9842
Epoch 1/2... Discriminator Loss: 0.4990... Generator Loss: 2.1662
Epoch 1/2... Discriminator Loss: 0.6579... Generator Loss: 1.6425
Epoch 1/2... Discriminator Loss: 0.4865... Generator Loss: 2.3526
Epoch 1/2... Discriminator Loss: 0.9316... Generator Loss: 1.7860
Epoch 1/2... Discriminator Loss: 0.5377... Generator Loss: 2.6908
Epoch 1/2... Discriminator Loss: 0.4632... Generator Loss: 3.4912
Epoch 1/2... Discriminator Loss: 0.5054... Generator Loss: 2.1200
Epoch 1/2... Discriminator Loss: 0.4675... Generator Loss: 2.3375
Epoch 1/2... Discriminator Loss: 0.4163... Generator Loss: 3.0784
Epoch 1/2... Discriminator Loss: 0.5495... Generator Loss: 2.4977
Epoch 1/2... Discriminator Loss: 0.4829... Generator Loss: 4.2868
Epoch 1/2... Discriminator Loss: 0.7008... Generator Loss: 1.4589
Epoch 1/2... Discriminator Loss: 1.4635... Generator Loss: 5.5818
Epoch 1/2... Discriminator Loss: 0.5364... Generator Loss: 3.7635
Epoch 1/2... Discriminator Loss: 4.4897... Generator Loss: 0.0378
Epoch 1/2... Discriminator Loss: 0.7031... Generator Loss: 1.6844
Epoch 1/2... Discriminator Loss: 0.7233... Generator Loss: 1.3611
Epoch 1/2... Discriminator Loss: 0.5308... Generator Loss: 2.2359
Epoch 1/2... Discriminator Loss: 0.4797... Generator Loss: 2.3775
Epoch 1/2... Discriminator Loss: 0.6599... Generator Loss: 1.4590
Epoch 1/2... Discriminator Loss: 0.5899... Generator Loss: 3.5591
Epoch 1/2... Discriminator Loss: 0.6999... Generator Loss: 4.2388
Epoch 1/2... Discriminator Loss: 0.4545... Generator Loss: 3.1467
Epoch 1/2... Discriminator Loss: 0.4409... Generator Loss: 2.5722
Epoch 1/2... Discriminator Loss: 0.6387... Generator Loss: 1.5947
Epoch 1/2... Discriminator Loss: 1.3463... Generator Loss: 0.6883
Epoch 1/2... Discriminator Loss: 1.2335... Generator Loss: 0.6996
Epoch 1/2... Discriminator Loss: 0.5922... Generator Loss: 1.8752
Epoch 1/2... Discriminator Loss: 0.7521... Generator Loss: 1.3490
Epoch 1/2... Discriminator Loss: 0.4637... Generator Loss: 2.8972
Epoch 1/2... Discriminator Loss: 0.5180... Generator Loss: 2.1549
Epoch 1/2... Discriminator Loss: 0.6086... Generator Loss: 1.6713
Epoch 1/2... Discriminator Loss: 0.7911... Generator Loss: 3.8892
Epoch 1/2... Discriminator Loss: 1.3536... Generator Loss: 1.3277
Epoch 1/2... Discriminator Loss: 1.0211... Generator Loss: 0.9563
Epoch 1/2... Discriminator Loss: 0.5877... Generator Loss: 2.3013
Epoch 1/2... Discriminator Loss: 0.8678... Generator Loss: 3.5990
Epoch 1/2... Discriminator Loss: 1.0857... Generator Loss: 0.8173
Epoch 1/2... Discriminator Loss: 0.5625... Generator Loss: 1.8942
Epoch 1/2... Discriminator Loss: 0.6036... Generator Loss: 1.6744
Epoch 1/2... Discriminator Loss: 1.1458... Generator Loss: 0.7345
Epoch 1/2... Discriminator Loss: 1.1959... Generator Loss: 1.2683
Epoch 1/2... Discriminator Loss: 0.6530... Generator Loss: 1.7015
Epoch 1/2... Discriminator Loss: 1.2665... Generator Loss: 0.6463
Epoch 1/2... Discriminator Loss: 0.7199... Generator Loss: 1.4843
Epoch 1/2... Discriminator Loss: 0.7872... Generator Loss: 3.7980
Epoch 1/2... Discriminator Loss: 0.5679... Generator Loss: 2.3983
Epoch 1/2... Discriminator Loss: 0.6454... Generator Loss: 1.6200
Epoch 1/2... Discriminator Loss: 0.9857... Generator Loss: 1.4537
Epoch 1/2... Discriminator Loss: 0.8704... Generator Loss: 1.0963
Epoch 1/2... Discriminator Loss: 0.6753... Generator Loss: 1.4408
Epoch 1/2... Discriminator Loss: 0.7370... Generator Loss: 2.1744
Epoch 1/2... Discriminator Loss: 0.5773... Generator Loss: 1.8419
Epoch 1/2... Discriminator Loss: 0.6472... Generator Loss: 2.0226
Epoch 1/2... Discriminator Loss: 0.6111... Generator Loss: 1.8245
Epoch 1/2... Discriminator Loss: 0.8308... Generator Loss: 1.1095
Epoch 1/2... Discriminator Loss: 1.4795... Generator Loss: 0.8889
Epoch 1/2... Discriminator Loss: 1.0452... Generator Loss: 3.2885
Epoch 1/2... Discriminator Loss: 0.7304... Generator Loss: 1.4023
Epoch 1/2... Discriminator Loss: 0.6476... Generator Loss: 1.6828
Epoch 1/2... Discriminator Loss: 0.6733... Generator Loss: 1.4971
Epoch 1/2... Discriminator Loss: 0.5287... Generator Loss: 2.0542
Epoch 1/2... Discriminator Loss: 0.6113... Generator Loss: 1.6093
Epoch 1/2... Discriminator Loss: 0.5250... Generator Loss: 1.9611
Epoch 1/2... Discriminator Loss: 0.8497... Generator Loss: 2.8978
Epoch 1/2... Discriminator Loss: 1.0280... Generator Loss: 2.3997
Epoch 1/2... Discriminator Loss: 0.7349... Generator Loss: 2.2126
Epoch 1/2... Discriminator Loss: 0.5369... Generator Loss: 2.0070
Epoch 1/2... Discriminator Loss: 0.6551... Generator Loss: 1.9430
Epoch 2/2... Discriminator Loss: 1.0312... Generator Loss: 0.8646
Epoch 2/2... Discriminator Loss: 0.8453... Generator Loss: 1.1301
Epoch 2/2... Discriminator Loss: 0.7050... Generator Loss: 2.5292
Epoch 2/2... Discriminator Loss: 0.5873... Generator Loss: 1.7159
Epoch 2/2... Discriminator Loss: 1.1384... Generator Loss: 2.9186
Epoch 2/2... Discriminator Loss: 0.9210... Generator Loss: 1.3300
Epoch 2/2... Discriminator Loss: 0.7539... Generator Loss: 1.9860
Epoch 2/2... Discriminator Loss: 0.9311... Generator Loss: 1.0658
Epoch 2/2... Discriminator Loss: 0.6866... Generator Loss: 1.5566
Epoch 2/2... Discriminator Loss: 0.6833... Generator Loss: 1.7211
Epoch 2/2... Discriminator Loss: 1.4857... Generator Loss: 0.5126
Epoch 2/2... Discriminator Loss: 0.9168... Generator Loss: 1.0281
Epoch 2/2... Discriminator Loss: 2.9245... Generator Loss: 5.8303
Epoch 2/2... Discriminator Loss: 1.2625... Generator Loss: 0.7287
Epoch 2/2... Discriminator Loss: 0.9755... Generator Loss: 0.9131
Epoch 2/2... Discriminator Loss: 0.7488... Generator Loss: 1.3315
Epoch 2/2... Discriminator Loss: 1.8488... Generator Loss: 0.4385
Epoch 2/2... Discriminator Loss: 0.8805... Generator Loss: 1.1559
Epoch 2/2... Discriminator Loss: 0.7058... Generator Loss: 1.4853
Epoch 2/2... Discriminator Loss: 0.7946... Generator Loss: 1.2592
Epoch 2/2... Discriminator Loss: 2.8395... Generator Loss: 0.2001
Epoch 2/2... Discriminator Loss: 0.6975... Generator Loss: 1.7087
Epoch 2/2... Discriminator Loss: 0.9628... Generator Loss: 0.9827
Epoch 2/2... Discriminator Loss: 0.7893... Generator Loss: 2.0487
Epoch 2/2... Discriminator Loss: 0.7230... Generator Loss: 1.4318
Epoch 2/2... Discriminator Loss: 0.7342... Generator Loss: 1.3525
Epoch 2/2... Discriminator Loss: 1.1758... Generator Loss: 0.7741
Epoch 2/2... Discriminator Loss: 1.2270... Generator Loss: 0.6192
Epoch 2/2... Discriminator Loss: 0.7793... Generator Loss: 1.6284
Epoch 2/2... Discriminator Loss: 0.8626... Generator Loss: 1.3540
Epoch 2/2... Discriminator Loss: 0.9692... Generator Loss: 0.9707
Epoch 2/2... Discriminator Loss: 0.7570... Generator Loss: 1.3815
Epoch 2/2... Discriminator Loss: 1.2334... Generator Loss: 0.6565
Epoch 2/2... Discriminator Loss: 1.1760... Generator Loss: 2.9876
Epoch 2/2... Discriminator Loss: 1.7421... Generator Loss: 0.7979
Epoch 2/2... Discriminator Loss: 0.7871... Generator Loss: 2.7112
Epoch 2/2... Discriminator Loss: 0.9019... Generator Loss: 1.0864
Epoch 2/2... Discriminator Loss: 1.0896... Generator Loss: 0.8472
Epoch 2/2... Discriminator Loss: 1.5195... Generator Loss: 0.7374
Epoch 2/2... Discriminator Loss: 0.7857... Generator Loss: 1.2675
Epoch 2/2... Discriminator Loss: 1.2998... Generator Loss: 0.6324
Epoch 2/2... Discriminator Loss: 0.7075... Generator Loss: 1.4801
Epoch 2/2... Discriminator Loss: 0.6276... Generator Loss: 1.8873
Epoch 2/2... Discriminator Loss: 0.9178... Generator Loss: 1.0571
Epoch 2/2... Discriminator Loss: 1.2587... Generator Loss: 0.7492
Epoch 2/2... Discriminator Loss: 0.9019... Generator Loss: 1.1020
Epoch 2/2... Discriminator Loss: 0.6600... Generator Loss: 1.8650
Epoch 2/2... Discriminator Loss: 3.2920... Generator Loss: 0.1287
Epoch 2/2... Discriminator Loss: 0.8119... Generator Loss: 1.6786
Epoch 2/2... Discriminator Loss: 1.0633... Generator Loss: 0.8534
Epoch 2/2... Discriminator Loss: 2.1352... Generator Loss: 0.3208
Epoch 2/2... Discriminator Loss: 1.6858... Generator Loss: 4.5229
Epoch 2/2... Discriminator Loss: 0.7109... Generator Loss: 2.3620
Epoch 2/2... Discriminator Loss: 0.8019... Generator Loss: 1.2494
Epoch 2/2... Discriminator Loss: 0.8064... Generator Loss: 1.7501
Epoch 2/2... Discriminator Loss: 0.6320... Generator Loss: 1.6201
Epoch 2/2... Discriminator Loss: 1.7878... Generator Loss: 0.4247
Epoch 2/2... Discriminator Loss: 2.3267... Generator Loss: 0.2994
Epoch 2/2... Discriminator Loss: 0.8133... Generator Loss: 1.5672
Epoch 2/2... Discriminator Loss: 0.9250... Generator Loss: 1.0149
Epoch 2/2... Discriminator Loss: 0.8473... Generator Loss: 1.2016
Epoch 2/2... Discriminator Loss: 0.8480... Generator Loss: 1.1481
Epoch 2/2... Discriminator Loss: 1.0043... Generator Loss: 0.8721
Epoch 2/2... Discriminator Loss: 0.6917... Generator Loss: 1.5320
Epoch 2/2... Discriminator Loss: 0.9971... Generator Loss: 0.8922
Epoch 2/2... Discriminator Loss: 0.8428... Generator Loss: 1.2636
Epoch 2/2... Discriminator Loss: 1.2152... Generator Loss: 0.7421
Epoch 2/2... Discriminator Loss: 1.2279... Generator Loss: 0.9139
Epoch 2/2... Discriminator Loss: 0.9135... Generator Loss: 1.1839
Epoch 2/2... Discriminator Loss: 0.7390... Generator Loss: 1.3247
Epoch 2/2... Discriminator Loss: 0.7532... Generator Loss: 1.3453
Epoch 2/2... Discriminator Loss: 0.6745... Generator Loss: 1.4779
Epoch 2/2... Discriminator Loss: 0.7384... Generator Loss: 2.0924
Epoch 2/2... Discriminator Loss: 0.7906... Generator Loss: 3.0497
Epoch 2/2... Discriminator Loss: 1.8407... Generator Loss: 0.4089
Epoch 2/2... Discriminator Loss: 1.4820... Generator Loss: 0.5424
Epoch 2/2... Discriminator Loss: 1.8467... Generator Loss: 0.3573
Epoch 2/2... Discriminator Loss: 1.7498... Generator Loss: 0.5792
Epoch 2/2... Discriminator Loss: 0.9218... Generator Loss: 1.0431
Epoch 2/2... Discriminator Loss: 0.9013... Generator Loss: 1.1321
Epoch 2/2... Discriminator Loss: 0.8602... Generator Loss: 1.8972
Epoch 2/2... Discriminator Loss: 0.6667... Generator Loss: 1.5900
Epoch 2/2... Discriminator Loss: 0.9047... Generator Loss: 1.2623
Epoch 2/2... Discriminator Loss: 0.8121... Generator Loss: 1.2082
Epoch 2/2... Discriminator Loss: 1.2797... Generator Loss: 0.7524
Epoch 2/2... Discriminator Loss: 1.2137... Generator Loss: 4.0091
Epoch 2/2... Discriminator Loss: 0.8872... Generator Loss: 1.1242
Epoch 2/2... Discriminator Loss: 0.9599... Generator Loss: 1.1170
Epoch 2/2... Discriminator Loss: 0.7789... Generator Loss: 1.3978
Epoch 2/2... Discriminator Loss: 0.8132... Generator Loss: 1.3544
Epoch 2/2... Discriminator Loss: 1.1553... Generator Loss: 0.7836
Epoch 2/2... Discriminator Loss: 0.8081... Generator Loss: 1.2357
Epoch 2/2... Discriminator Loss: 0.7752... Generator Loss: 1.2557
Epoch 2/2... Discriminator Loss: 0.8183... Generator Loss: 1.3025

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 2.1842... Generator Loss: 0.3172
Epoch 1/1... Discriminator Loss: 5.1264... Generator Loss: 0.1389
Epoch 1/1... Discriminator Loss: 1.8499... Generator Loss: 0.8107
Epoch 1/1... Discriminator Loss: 0.4847... Generator Loss: 3.1150
Epoch 1/1... Discriminator Loss: 1.0787... Generator Loss: 2.3384
Epoch 1/1... Discriminator Loss: 0.7781... Generator Loss: 1.7420
Epoch 1/1... Discriminator Loss: 0.6670... Generator Loss: 2.3396
Epoch 1/1... Discriminator Loss: 0.9396... Generator Loss: 1.1890
Epoch 1/1... Discriminator Loss: 1.9784... Generator Loss: 0.3027
Epoch 1/1... Discriminator Loss: 1.6317... Generator Loss: 0.6180
Epoch 1/1... Discriminator Loss: 0.8182... Generator Loss: 1.3577
Epoch 1/1... Discriminator Loss: 0.9827... Generator Loss: 1.5183
Epoch 1/1... Discriminator Loss: 0.8990... Generator Loss: 1.3298
Epoch 1/1... Discriminator Loss: 0.8934... Generator Loss: 1.2134
Epoch 1/1... Discriminator Loss: 0.8299... Generator Loss: 1.9066
Epoch 1/1... Discriminator Loss: 1.4083... Generator Loss: 0.7275
Epoch 1/1... Discriminator Loss: 0.8692... Generator Loss: 1.4142
Epoch 1/1... Discriminator Loss: 1.4536... Generator Loss: 0.4994
Epoch 1/1... Discriminator Loss: 0.7320... Generator Loss: 1.8795
Epoch 1/1... Discriminator Loss: 0.6933... Generator Loss: 1.6967
Epoch 1/1... Discriminator Loss: 1.0605... Generator Loss: 1.7072
Epoch 1/1... Discriminator Loss: 1.8159... Generator Loss: 3.6148
Epoch 1/1... Discriminator Loss: 2.2511... Generator Loss: 0.2701
Epoch 1/1... Discriminator Loss: 1.5734... Generator Loss: 3.9502
Epoch 1/1... Discriminator Loss: 1.0122... Generator Loss: 1.9311
Epoch 1/1... Discriminator Loss: 1.7336... Generator Loss: 0.6306
Epoch 1/1... Discriminator Loss: 0.9257... Generator Loss: 1.2299
Epoch 1/1... Discriminator Loss: 0.8269... Generator Loss: 1.7843
Epoch 1/1... Discriminator Loss: 1.6423... Generator Loss: 0.4582
Epoch 1/1... Discriminator Loss: 1.0229... Generator Loss: 1.4793
Epoch 1/1... Discriminator Loss: 1.2177... Generator Loss: 1.6967
Epoch 1/1... Discriminator Loss: 1.2033... Generator Loss: 0.7850
Epoch 1/1... Discriminator Loss: 1.1607... Generator Loss: 0.8557
Epoch 1/1... Discriminator Loss: 1.1272... Generator Loss: 0.9988
Epoch 1/1... Discriminator Loss: 1.3266... Generator Loss: 0.6411
Epoch 1/1... Discriminator Loss: 1.1118... Generator Loss: 1.3105
Epoch 1/1... Discriminator Loss: 1.2245... Generator Loss: 0.7314
Epoch 1/1... Discriminator Loss: 1.9494... Generator Loss: 0.3743
Epoch 1/1... Discriminator Loss: 1.0361... Generator Loss: 1.1525
Epoch 1/1... Discriminator Loss: 1.3729... Generator Loss: 0.6209
Epoch 1/1... Discriminator Loss: 1.1330... Generator Loss: 0.7992
Epoch 1/1... Discriminator Loss: 0.9310... Generator Loss: 1.5678
Epoch 1/1... Discriminator Loss: 1.1355... Generator Loss: 0.7449
Epoch 1/1... Discriminator Loss: 1.0602... Generator Loss: 0.9435
Epoch 1/1... Discriminator Loss: 0.8765... Generator Loss: 1.8264
Epoch 1/1... Discriminator Loss: 1.5633... Generator Loss: 0.4598
Epoch 1/1... Discriminator Loss: 1.1664... Generator Loss: 0.9302
Epoch 1/1... Discriminator Loss: 1.1108... Generator Loss: 1.7779
Epoch 1/1... Discriminator Loss: 1.4608... Generator Loss: 2.4102
Epoch 1/1... Discriminator Loss: 1.7464... Generator Loss: 0.4072
Epoch 1/1... Discriminator Loss: 1.4795... Generator Loss: 0.5181
Epoch 1/1... Discriminator Loss: 1.0374... Generator Loss: 1.1683
Epoch 1/1... Discriminator Loss: 1.9145... Generator Loss: 3.5639
Epoch 1/1... Discriminator Loss: 1.0000... Generator Loss: 1.5753
Epoch 1/1... Discriminator Loss: 1.1320... Generator Loss: 2.1229
Epoch 1/1... Discriminator Loss: 1.1830... Generator Loss: 0.7115
Epoch 1/1... Discriminator Loss: 0.9113... Generator Loss: 1.7694
Epoch 1/1... Discriminator Loss: 1.4444... Generator Loss: 0.5975
Epoch 1/1... Discriminator Loss: 0.9596... Generator Loss: 1.1185
Epoch 1/1... Discriminator Loss: 0.9619... Generator Loss: 1.3475
Epoch 1/1... Discriminator Loss: 1.0414... Generator Loss: 1.1423
Epoch 1/1... Discriminator Loss: 0.9789... Generator Loss: 1.1265
Epoch 1/1... Discriminator Loss: 1.0784... Generator Loss: 1.3950
Epoch 1/1... Discriminator Loss: 1.1263... Generator Loss: 1.1043
Epoch 1/1... Discriminator Loss: 2.0851... Generator Loss: 0.2587
Epoch 1/1... Discriminator Loss: 1.5177... Generator Loss: 0.4582
Epoch 1/1... Discriminator Loss: 1.2701... Generator Loss: 0.5898
Epoch 1/1... Discriminator Loss: 1.4951... Generator Loss: 2.3711
Epoch 1/1... Discriminator Loss: 1.0082... Generator Loss: 0.9046
Epoch 1/1... Discriminator Loss: 1.1995... Generator Loss: 1.5469
Epoch 1/1... Discriminator Loss: 1.6569... Generator Loss: 0.4192
Epoch 1/1... Discriminator Loss: 1.5700... Generator Loss: 0.4250
Epoch 1/1... Discriminator Loss: 1.8745... Generator Loss: 0.3198
Epoch 1/1... Discriminator Loss: 1.2241... Generator Loss: 2.3750
Epoch 1/1... Discriminator Loss: 1.0636... Generator Loss: 1.4730
Epoch 1/1... Discriminator Loss: 1.0685... Generator Loss: 1.5037
Epoch 1/1... Discriminator Loss: 1.1589... Generator Loss: 1.9679
Epoch 1/1... Discriminator Loss: 1.1613... Generator Loss: 1.2182
Epoch 1/1... Discriminator Loss: 1.2851... Generator Loss: 0.5958
Epoch 1/1... Discriminator Loss: 0.8798... Generator Loss: 1.3731
Epoch 1/1... Discriminator Loss: 1.3397... Generator Loss: 2.2799
Epoch 1/1... Discriminator Loss: 1.1074... Generator Loss: 1.7274
Epoch 1/1... Discriminator Loss: 0.9761... Generator Loss: 1.0071
Epoch 1/1... Discriminator Loss: 0.9203... Generator Loss: 1.2218
Epoch 1/1... Discriminator Loss: 1.7104... Generator Loss: 0.3972
Epoch 1/1... Discriminator Loss: 1.2385... Generator Loss: 0.6207
Epoch 1/1... Discriminator Loss: 1.3078... Generator Loss: 0.5716
Epoch 1/1... Discriminator Loss: 1.2017... Generator Loss: 0.9901
Epoch 1/1... Discriminator Loss: 1.4805... Generator Loss: 0.4657
Epoch 1/1... Discriminator Loss: 1.1931... Generator Loss: 1.3778
Epoch 1/1... Discriminator Loss: 1.1946... Generator Loss: 1.8832
Epoch 1/1... Discriminator Loss: 1.2033... Generator Loss: 0.6911
Epoch 1/1... Discriminator Loss: 1.3832... Generator Loss: 0.4903
Epoch 1/1... Discriminator Loss: 0.9519... Generator Loss: 1.0807
Epoch 1/1... Discriminator Loss: 2.0841... Generator Loss: 0.2843
Epoch 1/1... Discriminator Loss: 1.5258... Generator Loss: 0.5347
Epoch 1/1... Discriminator Loss: 1.4629... Generator Loss: 1.8686
Epoch 1/1... Discriminator Loss: 1.0970... Generator Loss: 1.0958
Epoch 1/1... Discriminator Loss: 0.9622... Generator Loss: 1.0821
Epoch 1/1... Discriminator Loss: 0.8517... Generator Loss: 1.4642
Epoch 1/1... Discriminator Loss: 1.4265... Generator Loss: 2.2744
Epoch 1/1... Discriminator Loss: 1.1051... Generator Loss: 0.9387
Epoch 1/1... Discriminator Loss: 1.5435... Generator Loss: 2.3601
Epoch 1/1... Discriminator Loss: 1.0154... Generator Loss: 1.6584
Epoch 1/1... Discriminator Loss: 1.1282... Generator Loss: 1.7528
Epoch 1/1... Discriminator Loss: 1.1690... Generator Loss: 1.0787
Epoch 1/1... Discriminator Loss: 1.3790... Generator Loss: 0.5385
Epoch 1/1... Discriminator Loss: 1.3401... Generator Loss: 1.9073
Epoch 1/1... Discriminator Loss: 1.1748... Generator Loss: 0.7401
Epoch 1/1... Discriminator Loss: 0.9138... Generator Loss: 1.0882
Epoch 1/1... Discriminator Loss: 1.2281... Generator Loss: 0.6422
Epoch 1/1... Discriminator Loss: 1.2360... Generator Loss: 0.6599
Epoch 1/1... Discriminator Loss: 0.8968... Generator Loss: 1.2192
Epoch 1/1... Discriminator Loss: 1.2480... Generator Loss: 0.7835
Epoch 1/1... Discriminator Loss: 1.2456... Generator Loss: 1.9010
Epoch 1/1... Discriminator Loss: 1.1749... Generator Loss: 0.7786
Epoch 1/1... Discriminator Loss: 1.1092... Generator Loss: 0.7664
Epoch 1/1... Discriminator Loss: 0.9520... Generator Loss: 1.2132
Epoch 1/1... Discriminator Loss: 0.9460... Generator Loss: 1.4191
Epoch 1/1... Discriminator Loss: 0.8935... Generator Loss: 1.5114
Epoch 1/1... Discriminator Loss: 1.0544... Generator Loss: 0.9725
Epoch 1/1... Discriminator Loss: 1.2332... Generator Loss: 0.8806
Epoch 1/1... Discriminator Loss: 1.5075... Generator Loss: 0.4627
Epoch 1/1... Discriminator Loss: 0.8929... Generator Loss: 1.3376
Epoch 1/1... Discriminator Loss: 1.2064... Generator Loss: 1.6041
Epoch 1/1... Discriminator Loss: 1.2370... Generator Loss: 0.7042
Epoch 1/1... Discriminator Loss: 1.1424... Generator Loss: 0.7872
Epoch 1/1... Discriminator Loss: 2.5814... Generator Loss: 3.6299
Epoch 1/1... Discriminator Loss: 1.5152... Generator Loss: 0.4692
Epoch 1/1... Discriminator Loss: 1.2669... Generator Loss: 0.7009
Epoch 1/1... Discriminator Loss: 1.0145... Generator Loss: 1.3519
Epoch 1/1... Discriminator Loss: 1.0949... Generator Loss: 1.4946
Epoch 1/1... Discriminator Loss: 1.0737... Generator Loss: 0.9330
Epoch 1/1... Discriminator Loss: 1.1698... Generator Loss: 0.8308
Epoch 1/1... Discriminator Loss: 0.8311... Generator Loss: 1.8877
Epoch 1/1... Discriminator Loss: 1.0810... Generator Loss: 0.9534
Epoch 1/1... Discriminator Loss: 1.0474... Generator Loss: 1.0239
Epoch 1/1... Discriminator Loss: 1.2353... Generator Loss: 0.6439
Epoch 1/1... Discriminator Loss: 1.0726... Generator Loss: 1.5813
Epoch 1/1... Discriminator Loss: 1.0603... Generator Loss: 0.8638
Epoch 1/1... Discriminator Loss: 1.4243... Generator Loss: 0.5174
Epoch 1/1... Discriminator Loss: 0.9714... Generator Loss: 1.1490
Epoch 1/1... Discriminator Loss: 1.2057... Generator Loss: 1.6759
Epoch 1/1... Discriminator Loss: 1.2201... Generator Loss: 2.0115
Epoch 1/1... Discriminator Loss: 0.9398... Generator Loss: 1.3241
Epoch 1/1... Discriminator Loss: 0.9817... Generator Loss: 1.0196
Epoch 1/1... Discriminator Loss: 1.0792... Generator Loss: 1.0673
Epoch 1/1... Discriminator Loss: 0.9877... Generator Loss: 1.0888
Epoch 1/1... Discriminator Loss: 1.0432... Generator Loss: 1.1569
Epoch 1/1... Discriminator Loss: 1.1579... Generator Loss: 0.7237
Epoch 1/1... Discriminator Loss: 2.4474... Generator Loss: 4.7296
Epoch 1/1... Discriminator Loss: 1.3179... Generator Loss: 0.6303
Epoch 1/1... Discriminator Loss: 1.1893... Generator Loss: 0.9440
Epoch 1/1... Discriminator Loss: 1.3634... Generator Loss: 0.6289
Epoch 1/1... Discriminator Loss: 0.9875... Generator Loss: 1.0836
Epoch 1/1... Discriminator Loss: 1.5008... Generator Loss: 0.4643
Epoch 1/1... Discriminator Loss: 1.2163... Generator Loss: 0.7061
Epoch 1/1... Discriminator Loss: 1.4354... Generator Loss: 0.5276
Epoch 1/1... Discriminator Loss: 1.2563... Generator Loss: 0.7181
Epoch 1/1... Discriminator Loss: 1.1434... Generator Loss: 0.8191
Epoch 1/1... Discriminator Loss: 0.9811... Generator Loss: 1.0450
Epoch 1/1... Discriminator Loss: 0.9294... Generator Loss: 1.6585
Epoch 1/1... Discriminator Loss: 0.9899... Generator Loss: 1.9251
Epoch 1/1... Discriminator Loss: 1.5136... Generator Loss: 2.7710
Epoch 1/1... Discriminator Loss: 1.2278... Generator Loss: 0.7624
Epoch 1/1... Discriminator Loss: 0.8735... Generator Loss: 1.3972
Epoch 1/1... Discriminator Loss: 0.9444... Generator Loss: 1.0874
Epoch 1/1... Discriminator Loss: 1.7903... Generator Loss: 0.3998
Epoch 1/1... Discriminator Loss: 0.9718... Generator Loss: 1.2015
Epoch 1/1... Discriminator Loss: 1.2151... Generator Loss: 0.7654
Epoch 1/1... Discriminator Loss: 0.9715... Generator Loss: 1.1544
Epoch 1/1... Discriminator Loss: 0.9637... Generator Loss: 1.3843
Epoch 1/1... Discriminator Loss: 1.4437... Generator Loss: 0.4871
Epoch 1/1... Discriminator Loss: 1.0950... Generator Loss: 2.3161
Epoch 1/1... Discriminator Loss: 1.9109... Generator Loss: 0.3428
Epoch 1/1... Discriminator Loss: 1.2057... Generator Loss: 1.2745
Epoch 1/1... Discriminator Loss: 1.3956... Generator Loss: 0.5956
Epoch 1/1... Discriminator Loss: 1.0638... Generator Loss: 1.0107
Epoch 1/1... Discriminator Loss: 1.1172... Generator Loss: 1.3021
Epoch 1/1... Discriminator Loss: 1.0449... Generator Loss: 0.8174
Epoch 1/1... Discriminator Loss: 1.5696... Generator Loss: 0.8628
Epoch 1/1... Discriminator Loss: 1.0286... Generator Loss: 1.2710
Epoch 1/1... Discriminator Loss: 1.1001... Generator Loss: 0.9567
Epoch 1/1... Discriminator Loss: 1.5084... Generator Loss: 0.4194
Epoch 1/1... Discriminator Loss: 0.6474... Generator Loss: 2.0328
Epoch 1/1... Discriminator Loss: 0.7494... Generator Loss: 1.6471
Epoch 1/1... Discriminator Loss: 1.0735... Generator Loss: 0.8587
Epoch 1/1... Discriminator Loss: 1.5427... Generator Loss: 0.5146
Epoch 1/1... Discriminator Loss: 1.8029... Generator Loss: 4.3640
Epoch 1/1... Discriminator Loss: 0.9354... Generator Loss: 1.0825
Epoch 1/1... Discriminator Loss: 1.3018... Generator Loss: 0.6281
Epoch 1/1... Discriminator Loss: 1.4540... Generator Loss: 0.5476
Epoch 1/1... Discriminator Loss: 0.9023... Generator Loss: 1.0380
Epoch 1/1... Discriminator Loss: 1.0338... Generator Loss: 2.1328
Epoch 1/1... Discriminator Loss: 1.6105... Generator Loss: 0.4261
Epoch 1/1... Discriminator Loss: 0.9739... Generator Loss: 1.7485
Epoch 1/1... Discriminator Loss: 0.9093... Generator Loss: 1.1916
Epoch 1/1... Discriminator Loss: 1.1110... Generator Loss: 0.7447
Epoch 1/1... Discriminator Loss: 2.3067... Generator Loss: 0.1997
Epoch 1/1... Discriminator Loss: 1.2730... Generator Loss: 0.6950
Epoch 1/1... Discriminator Loss: 0.9808... Generator Loss: 1.4875
Epoch 1/1... Discriminator Loss: 1.0248... Generator Loss: 1.0797
Epoch 1/1... Discriminator Loss: 1.5991... Generator Loss: 2.4887
Epoch 1/1... Discriminator Loss: 1.1830... Generator Loss: 2.3109
Epoch 1/1... Discriminator Loss: 1.7871... Generator Loss: 4.1204
Epoch 1/1... Discriminator Loss: 1.0481... Generator Loss: 0.8575
Epoch 1/1... Discriminator Loss: 1.2365... Generator Loss: 0.6362
Epoch 1/1... Discriminator Loss: 1.3921... Generator Loss: 0.6213
Epoch 1/1... Discriminator Loss: 1.2510... Generator Loss: 3.2686
Epoch 1/1... Discriminator Loss: 0.9590... Generator Loss: 1.4167
Epoch 1/1... Discriminator Loss: 1.0446... Generator Loss: 1.0133
Epoch 1/1... Discriminator Loss: 1.0147... Generator Loss: 1.5842
Epoch 1/1... Discriminator Loss: 0.8908... Generator Loss: 2.7468
Epoch 1/1... Discriminator Loss: 1.1095... Generator Loss: 0.8518
Epoch 1/1... Discriminator Loss: 0.8017... Generator Loss: 1.4000
Epoch 1/1... Discriminator Loss: 0.8773... Generator Loss: 1.3215
Epoch 1/1... Discriminator Loss: 1.2789... Generator Loss: 0.7077
Epoch 1/1... Discriminator Loss: 0.8457... Generator Loss: 1.9636
Epoch 1/1... Discriminator Loss: 1.1840... Generator Loss: 1.8008
Epoch 1/1... Discriminator Loss: 1.5151... Generator Loss: 2.9292
Epoch 1/1... Discriminator Loss: 0.9009... Generator Loss: 1.8177
Epoch 1/1... Discriminator Loss: 1.1488... Generator Loss: 0.7460
Epoch 1/1... Discriminator Loss: 1.0437... Generator Loss: 0.9775
Epoch 1/1... Discriminator Loss: 0.8982... Generator Loss: 1.6459
Epoch 1/1... Discriminator Loss: 1.3181... Generator Loss: 2.4318
Epoch 1/1... Discriminator Loss: 1.8038... Generator Loss: 0.3485
Epoch 1/1... Discriminator Loss: 1.9391... Generator Loss: 0.3472
Epoch 1/1... Discriminator Loss: 0.8868... Generator Loss: 1.8005
Epoch 1/1... Discriminator Loss: 1.1613... Generator Loss: 0.7393
Epoch 1/1... Discriminator Loss: 2.0103... Generator Loss: 0.3015
Epoch 1/1... Discriminator Loss: 0.9184... Generator Loss: 2.1605
Epoch 1/1... Discriminator Loss: 0.9932... Generator Loss: 0.9388
Epoch 1/1... Discriminator Loss: 0.7810... Generator Loss: 1.2903
Epoch 1/1... Discriminator Loss: 0.9515... Generator Loss: 0.9799
Epoch 1/1... Discriminator Loss: 1.0425... Generator Loss: 3.0237
Epoch 1/1... Discriminator Loss: 0.6723... Generator Loss: 1.8633
Epoch 1/1... Discriminator Loss: 1.0094... Generator Loss: 0.9303
Epoch 1/1... Discriminator Loss: 0.8824... Generator Loss: 1.2293
Epoch 1/1... Discriminator Loss: 0.9797... Generator Loss: 1.0491
Epoch 1/1... Discriminator Loss: 1.5327... Generator Loss: 0.5140
Epoch 1/1... Discriminator Loss: 1.0050... Generator Loss: 1.1740
Epoch 1/1... Discriminator Loss: 0.9183... Generator Loss: 1.0200
Epoch 1/1... Discriminator Loss: 1.0106... Generator Loss: 0.9774
Epoch 1/1... Discriminator Loss: 1.2802... Generator Loss: 0.6611
Epoch 1/1... Discriminator Loss: 1.1830... Generator Loss: 0.7521
Epoch 1/1... Discriminator Loss: 1.5537... Generator Loss: 0.4972
Epoch 1/1... Discriminator Loss: 0.6909... Generator Loss: 2.0630
Epoch 1/1... Discriminator Loss: 0.6869... Generator Loss: 1.6397
Epoch 1/1... Discriminator Loss: 1.2382... Generator Loss: 0.7094
Epoch 1/1... Discriminator Loss: 0.7861... Generator Loss: 1.4709
Epoch 1/1... Discriminator Loss: 1.4091... Generator Loss: 0.5689
Epoch 1/1... Discriminator Loss: 0.8664... Generator Loss: 1.3525
Epoch 1/1... Discriminator Loss: 1.9671... Generator Loss: 0.2884
Epoch 1/1... Discriminator Loss: 0.7857... Generator Loss: 1.7847
Epoch 1/1... Discriminator Loss: 0.5901... Generator Loss: 2.0343
Epoch 1/1... Discriminator Loss: 1.0465... Generator Loss: 0.8406
Epoch 1/1... Discriminator Loss: 0.6608... Generator Loss: 1.6113
Epoch 1/1... Discriminator Loss: 0.9273... Generator Loss: 2.4315
Epoch 1/1... Discriminator Loss: 1.0432... Generator Loss: 0.8687
Epoch 1/1... Discriminator Loss: 0.9161... Generator Loss: 1.1582
Epoch 1/1... Discriminator Loss: 1.5346... Generator Loss: 0.5069
Epoch 1/1... Discriminator Loss: 1.6779... Generator Loss: 0.3841
Epoch 1/1... Discriminator Loss: 0.8797... Generator Loss: 1.2213
Epoch 1/1... Discriminator Loss: 0.9414... Generator Loss: 2.0677
Epoch 1/1... Discriminator Loss: 0.9790... Generator Loss: 1.0212
Epoch 1/1... Discriminator Loss: 0.7168... Generator Loss: 2.2149
Epoch 1/1... Discriminator Loss: 0.8299... Generator Loss: 1.7314
Epoch 1/1... Discriminator Loss: 2.4179... Generator Loss: 0.1875
Epoch 1/1... Discriminator Loss: 0.8184... Generator Loss: 1.2086
Epoch 1/1... Discriminator Loss: 1.9509... Generator Loss: 0.3232
Epoch 1/1... Discriminator Loss: 0.8400... Generator Loss: 1.1633
Epoch 1/1... Discriminator Loss: 0.9000... Generator Loss: 3.6686
Epoch 1/1... Discriminator Loss: 1.1099... Generator Loss: 0.8188
Epoch 1/1... Discriminator Loss: 0.6942... Generator Loss: 2.3091
Epoch 1/1... Discriminator Loss: 1.2391... Generator Loss: 0.6669
Epoch 1/1... Discriminator Loss: 2.5403... Generator Loss: 0.1796
Epoch 1/1... Discriminator Loss: 0.7980... Generator Loss: 2.5957
Epoch 1/1... Discriminator Loss: 0.9170... Generator Loss: 1.4086
Epoch 1/1... Discriminator Loss: 0.6656... Generator Loss: 1.6194
Epoch 1/1... Discriminator Loss: 1.4998... Generator Loss: 0.4847
Epoch 1/1... Discriminator Loss: 1.7784... Generator Loss: 3.3899
Epoch 1/1... Discriminator Loss: 0.7157... Generator Loss: 2.4416
Epoch 1/1... Discriminator Loss: 1.7857... Generator Loss: 0.3836
Epoch 1/1... Discriminator Loss: 0.8728... Generator Loss: 1.1161
Epoch 1/1... Discriminator Loss: 0.7191... Generator Loss: 1.7407
Epoch 1/1... Discriminator Loss: 1.2359... Generator Loss: 0.6728
Epoch 1/1... Discriminator Loss: 1.4411... Generator Loss: 3.2433
Epoch 1/1... Discriminator Loss: 0.7181... Generator Loss: 1.5420
Epoch 1/1... Discriminator Loss: 0.7805... Generator Loss: 1.4801
Epoch 1/1... Discriminator Loss: 0.7280... Generator Loss: 1.6892
Epoch 1/1... Discriminator Loss: 0.5803... Generator Loss: 1.7924
Epoch 1/1... Discriminator Loss: 1.4233... Generator Loss: 0.6069
Epoch 1/1... Discriminator Loss: 1.1207... Generator Loss: 0.8038
Epoch 1/1... Discriminator Loss: 1.0252... Generator Loss: 0.8334
Epoch 1/1... Discriminator Loss: 0.6824... Generator Loss: 3.1336
Epoch 1/1... Discriminator Loss: 0.8330... Generator Loss: 1.2374
Epoch 1/1... Discriminator Loss: 0.9910... Generator Loss: 0.9805
Epoch 1/1... Discriminator Loss: 0.8462... Generator Loss: 1.2380
Epoch 1/1... Discriminator Loss: 1.2349... Generator Loss: 4.5403
Epoch 1/1... Discriminator Loss: 0.7361... Generator Loss: 1.3962
Epoch 1/1... Discriminator Loss: 0.4740... Generator Loss: 2.7663
Epoch 1/1... Discriminator Loss: 0.5988... Generator Loss: 1.7915
Epoch 1/1... Discriminator Loss: 2.4292... Generator Loss: 0.2465
Epoch 1/1... Discriminator Loss: 0.7065... Generator Loss: 2.1466
Epoch 1/1... Discriminator Loss: 0.5783... Generator Loss: 1.9396
Epoch 1/1... Discriminator Loss: 0.9235... Generator Loss: 1.1145
Epoch 1/1... Discriminator Loss: 0.6549... Generator Loss: 1.8156
Epoch 1/1... Discriminator Loss: 0.7661... Generator Loss: 2.1149
Epoch 1/1... Discriminator Loss: 1.4003... Generator Loss: 0.5990
Epoch 1/1... Discriminator Loss: 0.9739... Generator Loss: 1.9034
Epoch 1/1... Discriminator Loss: 0.7297... Generator Loss: 2.1883
Epoch 1/1... Discriminator Loss: 0.6196... Generator Loss: 2.0657
Epoch 1/1... Discriminator Loss: 0.8008... Generator Loss: 1.6680
Epoch 1/1... Discriminator Loss: 1.2364... Generator Loss: 0.7176
Epoch 1/1... Discriminator Loss: 0.5735... Generator Loss: 1.9072
Epoch 1/1... Discriminator Loss: 0.6758... Generator Loss: 1.6318

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.